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This paper presents a new structured light approach dedicated to 3D mapping of underwater galleries in karst aquifers. This kind of method is based on the projection of a light pattern onto the scene captured by a camera. Our originality comes from the projected pattern used, since unlike literature methods, our light projector is a simple conical diving light. With a light cone, the recovered pattern in the image is a closed 2D curve we extract with a light contour detection method we have developed. A specific calibration method has also been created to estimate the cone geometry with respect to the camera. Therefore, we get a calibrated projector-camera pair, we can use to find matches between projected and recovered patterns. Our last contribution is the recovery of 3D data by triangulation using the camera cone relationship and the extracted closed 2D curve. The experimental results that will be presented in this article show the feasibility of the method.

International audience; This work presents the 3D reconstruction of the archeology objects in underwater environment. The videos and images are obtained from a calibrated camera system. The features of interest between image pairs are selected by the FAST detector and FLANN descriptor. Subsequently, the RANSAC method is applied to reject outlier points. The putative inliers are matched by triangulation to produce sparse point clouds in 3D space, using a pinhole camera model and Euclidean distance estimation. The 3D model textrue is achieved through a commond 3D Delaunay triangulation procedure. Moreover, evaluation of the quality of the underwater 3D reconstruction model to find its accuracy is done by measurement, comparing it with a 3D industrial scan model as well as a real object.

International audience; The objective of this paper is to develop 3D underwater reconstruction of archeology object, which is based on a mono-camera. The underwater images are obtained from a calibrated camera system. We first solve the problem of image processing by applying the well-known filter, therefore to improve quality of underwater images. The features of interest between image pairs are selected by well-known methods: a FAST detector and FLANN descriptor. Subsequently, the RANSAC method is applied to reject outlier points. The putative inliers are matched by triangulation to produce sparse point clouds in 3D space, using a pinhole camera model and Euclidean distance estimation.

We have shown that it's possible to realize a stereoscopic sensor with poor cameras. We developed image processing that is robust and allows us to quickly obtain results for the matching algorithm. We computed an important number of features on each segment, and with these features, we built 16-component vector used in the classification step. After an exhaustive study, we decided to combine two methods, Bayesian and neural, to construct an efficient classifier. The tests for indoor images had better than 90% good matching. With segment couples, it is possible to compute the 3D coordinates of the objects. Therefore, the mobile robot is able to localize and move about in the environment.

A laser range finder mounted on a site and azimuth turret is used as a 3D range camera. It forms, associated with a video camera, an original stereovision system. The internal structure of both images are the same but the resolution of 3D image stays low. By ignoring the acquiring speed of measures, spatial resolution is limited by the accuracy of deviation device and the laser footprint. The fact that the impact of the beam is not a point introduces spatial integration.To correct the average at depth discontinuities due to the beam footprint, a neural-network-based solution is reported.The use of such a multisensor system requires its calibration. As camera calibration is a well-known problem, the paper focuses on models and calibration methods of the range finder. Experimental results illustrate the quality of the calibration step in terms of accuracy and stability.The footprint correction is evaluated for both 1D and 2D range finder scannings.

International audience; The underwater 3D reconstruction cartography has made great progress in the last decade. The work presented in this paper is about the analysis and 3D reconstruction of archeological objects. Using a calibrated single camera and an uncalibrated system, we propose to describe a method to perform the Euclidian 3D reconstruction of unknown objects. A comparison of two methods is presented and tested on synthetic and real underwater pictures. Filters are proposed to simulate underwater environment and inherent problems. Finally, robust and stable features have been extracted from underwater pictures and used to perform the 3D model.

International audience; The underwater cartography has made great progress in the last decade. In this paper, we discuss of the 3D underwater cartography problem and propose a multimodal fusion approach. The work presented in this paper is about the analyze and 3D reconstruction of archeological objects. Using an uncalibrated single camera, we propose to describe a method to firstly, calibrate this camera integrating variation of the water optical refractive index to the distortion model. And secondly, robust and stable features have been extracted. A comparison of two methods of detecting and matching points is presented. Finally, we estimate the movement between different images and compare two methods.

International audience; This article presents a new method for estimating the pose of para-catadioptric vision systems. It is based on the estimation of vanishing points associated with vertical edges of the environment. However, unlike classical approaches no feature (line, circle) extraction and/or identification is needed. A sampled domain of possible vanishing points is tested and histograms are build to characterize the soundness of these points. A specificity index allows to find the more relevant histogram and the pose of the sensor. This method has been tested on simulated and real images giving very promising results (maximum angular error of 0.18 degree).

International audience; In robot-assisted beating heart surgery, motion of the heart surface might be virtually stabilized to let the surgeon work as in on-pump cardiac surgery. Virtual stabilization means to compensate physically the relative motion between the instrument tool tip and the region of interest on the heart surface, and to offer surgeon a stable visual display of the scene. To this end, motion of the heart must be estimated. This article focusses on motion estimation of the heart surface. Two approaches are considered in the paper. The first one is based on landmark tracking allowing 3D pose estimation. The second is based on texture tracking. Classical computer vision methods, as well as a new texture-based tracking scheme has been applied to track the heart motion, and, when possible, reconstruct 3D distance to the heart surface. Experimental results obtained on in vivo images show the estimated motion of heart surface points.

International audience; In robotic assisted beating heart surgery, motion of the heart surface might be virtually stabilized to let the surgeon work as in on-pump cardiac surgery. Virtual stabilization means to compensate physically the relative motion between instrument tool tip and point of interest on the heart surface, and to offer surgeon a stable visual display of the scene. In this way, motion of the heart must be estimated. This article focus on motion estimation of heart surface. Classical computer vision method has been applied to reconstruct 3D pose of interest point. Experimental results obtained on in vivo images show the estimated motion of heart surface points.

Our problem is a diagnostic task. Due to environment degraded conditions, direct measurements are not possible. Due to the rapidity of the machine, human intervention is not possible in case of position fault. So, an oriented vision solution is proposed. The problem must be solved for high velocity industrial tooling machines. Degraded conditions: vibrations, water and chip of metal projections, dazzling..., are present all the time. Image analysis in constraint environment depends on constraint importance. Before tooling, the vision system has to answer: “is it the right piece at the right place?” Complementary methods presented in this paper are proposed in an adaptive way to solve this diagnostic problem. This detection is made by comparing an acquired camera image of the piece to be tooled with an image of reference. Some image processing methods are performed and combined in order to extract 2D features. Some of these 2D features are evaluated and used as parameters in a diagnostic process. After a data analysis, image parameters are reduced. First, we present an overview of image processing methods generally used to solve that kind of problem and their limitations in our particular degraded case. In order to obtain automatic and robust classification, two methods are implemented. The first one is based on Bayes technique that provides a good classification in case of fault presence. The second method is based on neural networks and provides good results in case of images without faults. These two methods give a global rate of good classification greater than 90%, for 720 images acquired from an industrial site. Keywords: diagnostic task, 2D vision, video camera, image matching, bayes classifier, neural classifier Global Journal of Pure and Applied Sciences Vol. 12(2) 2006: 229-238

In order to permit the localization and the navigation of a mobile robot within an interior environment, we have built a stereoscopic sensor and implemented all the algorithms which allow to obtain 3D coordinates of real objects from data images. Sensor uses two mini cameras with vertical disposition. Processing on the images leads us to have segments on the two images. To match segments, we have to answer at the question: “Is there two segments, in a pair from top and bottom images segments, are two views of the same part of the real world of the robot ?”. For choosing the best matching algorithm, we compare three approaches: epipolar , Bayesian and neuronal techniques. In the epipolar method, we use only geometrical features. Two images of a single scene/object are related by the epipolar geometry, which can be describe by a 3x3 singular matrix called the fundamental matrix. It captures all geometric information contained in two images, and its determination is very important in many applications such as scene modeling and navigation of a mobile robot. With the two others, we associate to each segment, a set of 16 parameters which includes geometrical, gray level, textural and neighborhood features. For the comparison of these approaches, we have built a database of over 2500 segments. Results about this work are presented in our paper. Results with the two techniques from the data classification, are very good. In order to increase the quality of the segments matching, it is possible to find a combination rule for these two approaches. But, even if for the epipolar method 22% segments are not recognized, we think it is interesting to introduce it with the aim to obtain better results in some difficult configurations, by example when gray levels and textures are not very different in the scene, and also in order to initialize the classification algorithm. KEY WORDS: . Global Jnl Pure & Applied Sciences Vol.10(2) 2004: 335-341

The work we present here is a diagnostic task, which must be solved for high velocity industrial tooling machines URANE-20. Due to environment degraded conditions, direct measurements are not possible, also for rapidity of the machine, human intervention is not possible in case of position fault. Therefore, an oriented vision solution is proposed. Degraded conditions are vibrations, dazzling, water and chips of metal projections. In this case, the once method cannot achieve a diagnostic problem: is it the right piece at the right place? That is why complementary methods presented in this paper are proposed in an adaptive way to solve this diagnostic problem. Image processing methods allow us to find image parameters. After a data analysis, image parameters are reduced. Then, using Bayesian approach and neural approach, it is possible to ensure the diagnostic result. With these two methods, we obtain encouraging results and we show that it is possible to improve the results by combining different classifiers approaches.

According to the problem to be dealt with, the use of data acquisition system requires the knowledge of several models. In the case of the laser range-finder mounted on a site and azimuth turret, the geometrical model defines the co-ordinate transformation between measurement and reference frames. The paper shows the interest of a global calibration in comparison with a more classical approach which divides the problem into an internal model and external one. During the acquisition process another model called 'physical model' may be useful. It takes into account the fact the laser beam impact is not at a point and introduces a spatial integration. The so-called footprint effect limits the lateral resolution of the range finder at depth discontinuities. In order to correct that effect, an inverse physical model, based on neural networks, is proposed. The improvement of the footprint correction is then evaluated for a 2D and 3D scanning.

Performing motion tracking in real-time is an old and recurrent problem in Computer Vision. It has been addressed through a large set of approaches [17], [9], [1], but achieving a high level of robustness is still a challenge, especially with low definition input. In the considered application, tracking the heart motion in endoscopic beating heart sequences, the sensitivity of existing algorithms to visual artifacts and variations in illumination is an issue that calls for improvements. In the prospect of developing a motion compensation architecture for robotically assisted beating heart surgery, we address the problem of visual information retrieval by proposing a new Composite Tracking Algorithm using both template matching and texture analysis. As we will show in this paper, the use of texture characterization of the heart surface improves the overall precision and robustness, in comparison with other prior approaches.